Book Image

Mastering PyTorch

By : Ashish Ranjan Jha
Book Image

Mastering PyTorch

By: Ashish Ranjan Jha

Overview of this book

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (20 chapters)
1
Section 1: PyTorch Overview
4
Section 2: Working with Advanced Neural Network Architectures
8
Section 3: Generative Models and Deep Reinforcement Learning
13
Section 4: PyTorch in Production Systems

Exporting universal PyTorch models using TorchScript and ONNX

We have discussed serving PyTorch models extensively in the previous sections of this chapter, which is perhaps the most critical aspect of operationalizing PyTorch models in production systems. In this section, we will look at another important aspect – exporting PyTorch models. We have already learned how to save PyTorch models and load them back from disk in the classic Python scripting environment. But we need more ways of exporting PyTorch models. Why?

Well, for starters, the Python interpreter allows only one thread to run at a time using the global interpreter lock (GIL). This keeps us from parallelizing operations. Secondly, Python might not be supported in every system or device that we might want to run our models on. To address these problems, PyTorch offers support for exporting its models in an efficient format and in a platform- or language-agnostic manner such that a model can be run in environments...